Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales
Search
Open menu
Home
Papers
2102.03322
Cited By
v1
v2
v3
v4 (latest)
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
5 February 2021
Ana Lucic
Maartje ter Hoeve
Gabriele Tolomei
Maarten de Rijke
Fabrizio Silvestri
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks"
50 / 102 papers shown
TrendGNN: Towards Understanding of Epidemics, Beliefs, and Behaviors
Mulin Tian
Ajitesh Srivastava
AI4TS
110
0
0
29 Nov 2025
ARM-Explainer -- Explaining and improving graph neural network predictions for the maximum clique problem using node features and association rule mining
Bharat Sharman
Elkafi Hassini
216
0
0
28 Nov 2025
Interpreting GFlowNets for Drug Discovery: Extracting Actionable Insights for Medicinal Chemistry
Amirtha Varshini A S
Duminda S. Ranasinghe
Hok Hei Tam
91
0
0
24 Nov 2025
InteractiveGNNExplainer: A Visual Analytics Framework for Multi-Faceted Understanding and Probing of Graph Neural Network Predictions
International Conference on Information Visualisation (IV), 2025
TC Singh
Sougata Mukherjea
174
0
0
17 Nov 2025
MUSE-Explainer: Counterfactual Explanations for Symbolic Music Graph Classification Models
Baptiste Hilaire
E. Karystinaios
Gerhard Widmer
110
0
0
30 Sep 2025
Mitigating Popularity Bias in Counterfactual Explanations using Large Language Models
ACM Conference on Recommender Systems (RecSys), 2025
Arjan Hasami
Masoud Mansoury
205
0
0
12 Aug 2025
Learning to Locate: GNN-Powered Vulnerability Path Discovery in Open Source Code
Nima Atashin
Behrouz Tork Ladani
Mohammadreza Sharbaf
135
1
0
23 Jul 2025
Generalizability vs. Counterfactual Explainability Trade-Off
Fabiano Veglianti
Flavio Giorgi
Fabrizio Silvestri
Gabriele Tolomei
275
0
0
29 May 2025
Graph Inverse Style Transfer for Counterfactual Explainability
Bardh Prenkaj
Efstratios Zaradoukas
Gjergji Kasneci
AAML
344
1
0
23 May 2025
Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks
International Joint Conference on Artificial Intelligence (IJCAI), 2024
Han Zhang
Yan Wang
Guanfeng Liu
Pengfei Ding
Huaxiong Wang
Kwok-Yan Lam
480
0
0
20 May 2025
Interpreting Graph Inference with Skyline Explanations
Dazhuo Qiu
Haolai Che
Arijit Khan
Yinghui Wu
523
0
0
12 May 2025
COMRECGC: Global Graph Counterfactual Explainer through Common Recourse
Gregoire Fournier
Sourav Medya
BDL
429
1
0
11 May 2025
Robustness questions the interpretability of graph neural networks: what to do?
Kirill Lukyanov
Georgii Sazonov
Serafim Boyarsky
Ilya Makarov
AAML
1.0K
3
0
05 May 2025
DiCE-Extended: A Robust Approach to Counterfactual Explanations in Machine Learning
Volkan Bakir
Polat Goktas
Sureyya Akyuz
329
2
0
26 Apr 2025
Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification
Jonas Teufel
Annika Leinweber
Pascal Friederich
465
4
0
03 Apr 2025
Interpretability of Graph Neural Networks to Assess Effects of Global Change Drivers on Ecological Networks
Emré Anakok
Pierre Barbillon
Colin Fontaine
Elisa Thébault
460
2
0
19 Mar 2025
Recent Advances in Malware Detection: Graph Learning and Explainability
Hossein Shokouhinejad
Roozbeh Razavi-Far
Hesamodin Mohammadian
Mahdi Rabbani
Samuel Ansong
Griffin Higgins
Ali Ghorbani
AAML
710
19
0
14 Feb 2025
Natural Language Counterfactual Explanations for Graphs Using Large Language Models
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Flavio Giorgi
Cesare Campagnano
Fabrizio Silvestri
Gabriele Tolomei
LRM
432
4
0
28 Jan 2025
On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach
Neural Networks (NN), 2022
Ruichu Cai
Yuxuan Zhu
Xuexin Chen
Yuan Fang
Ruibing Jin
Jie Qiao
Zijian Li
540
13
0
31 Dec 2024
GraphXAIN: Narratives to Explain Graph Neural Networks
Mateusz Cedro
David Martens
570
7
0
04 Nov 2024
MBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings
Ashkan Golgoon
Ryan Franks
Khashayar Filom
Arjun Ravi Kannan
386
0
0
01 Nov 2024
Global Graph Counterfactual Explanation: A Subgraph Mapping Approach
Yinhan He
Wendy Zheng
Yaochen Zhu
Jing Ma
Saumitra Mishra
Natraj Raman
Ninghao Liu
Jundong Li
229
3
0
25 Oct 2024
Interpreting Temporal Graph Neural Networks with Koopman Theory
Michele Guerra
Simone Scardapane
F. Bianchi
FAtt
AI4TS
AI4CE
253
3
0
17 Oct 2024
Towards Few-shot Self-explaining Graph Neural Networks
Jingyu Peng
Qi Liu
Linan Yue
Zaixi Zhang
Kai Zhang
Yunhao Sha
MILM
285
5
0
14 Aug 2024
LLM-Powered Explanations: Unraveling Recommendations Through Subgraph Reasoning
Guangsi Shi
Xiaofeng Deng
Linhao Luo
Lijuan Xia
Lei Bao
Bei Ye
Fei Du
Shirui Pan
Yuxiao Li
322
17
0
22 Jun 2024
Explainable Graph Neural Networks Under Fire
Zhong Li
Simon Geisler
Yuhang Wang
Stephan Günnemann
M. Leeuwen
AAML
306
5
0
10 Jun 2024
GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks
Hsiao-Ying Lu
Yiran Li
Ujwal Pratap Krishna Kaluvakolanu Thyagarajan
K. Ma
453
1
0
06 Jun 2024
Explaining Expert Search and Team Formation Systems with ExES
Kiarash Golzadeh
Lukasz Golab
Jaroslaw Szlichta
291
2
0
21 May 2024
Design Requirements for Human-Centered Graph Neural Network Explanations
Pantea Habibi
Peyman Baghershahi
Sourav Medya
Debaleena Chattopadhyay
213
3
0
11 May 2024
Generating Robust Counterfactual Witnesses for Graph Neural Networks
Dazhuo Qiu
Mengying Wang
Arijit Khan
Yinghui Wu
327
6
0
30 Apr 2024
Global Concept Explanations for Graphs by Contrastive Learning
Jonas Teufel
Pascal Friederich
290
3
0
25 Apr 2024
CoDy: Counterfactual Explainers for Dynamic Graphs
Zhan Qu
Daniel Gomm
Michael Färber
CML
FAtt
351
1
0
25 Mar 2024
Fast Inference of Removal-Based Node Influence
The Web Conference (WWW), 2024
Weikai Li
Zhiping Xiao
Xiao Luo
Luke Huan
AAML
280
6
0
13 Mar 2024
Position: Topological Deep Learning is the New Frontier for Relational Learning
Theodore Papamarkou
Tolga Birdal
Michael M. Bronstein
Gunnar Carlsson
Justin Curry
...
Petar Velickovic
Bei Wang
Yusu Wang
Guo-Wei Wei
Ghada Zamzmi
AI4CE
377
73
0
14 Feb 2024
Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation
Xuexin Chen
Ruichu Cai
Zhengting Huang
Yuxuan Zhu
Julien Horwood
Zhifeng Hao
Zijian Li
Jose Miguel Hernandez-Lobato
AAML
567
5
0
13 Feb 2024
Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks
Jiahua Rao
Jiancong Xie
Hanjing Lin
Shuangjia Zheng
Zhen Wang
Yuedong Yang
272
2
0
07 Feb 2024
GOAt: Explaining Graph Neural Networks via Graph Output Attribution
International Conference on Learning Representations (ICLR), 2024
Shengyao Lu
Keith G. Mills
Jiao He
Bang Liu
Di Niu
FAtt
342
16
0
26 Jan 2024
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook
Wenzhao Jiang
Hao Liu
Hui Xiong
CML
AI4CE
590
10
0
19 Dec 2023
Exploring Causal Learning through Graph Neural Networks: An In-depth Review
Simi Job
Xiaohui Tao
Taotao Cai
Haoran Xie
Lin Li
Jianming Yong
Qing Li
CML
AI4CE
275
24
0
25 Nov 2023
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion
Jialin Chen
Shirley Wu
Abhijit Gupta
Rex Ying
DiffM
303
8
0
30 Oct 2023
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
Neural Information Processing Systems (NeurIPS), 2023
Yongqiang Chen
Yatao Bian
Kaiwen Zhou
Binghui Xie
Bo Han
James Cheng
OOD
309
65
0
29 Oct 2023
A Causal Disentangled Multi-Granularity Graph Classification Method
Yuan Li
Li Liu
Penggang Chen
Youmin Zhang
Guoyin Wang
179
1
0
25 Oct 2023
Graph AI in Medicine
Ruth Johnson
Michelle M. Li
Ayush Noori
Owen Queen
Marinka Zitnik
429
4
0
20 Oct 2023
RekomGNN: Visualizing, Contextualizing and Evaluating Graph Neural Networks Recommendations
C. Brumar
G. Appleby
Jen Rogers
Teddy Matinde
Lara Thompson
Remco Chang
Anamaria Crisan
HAI
331
1
0
17 Oct 2023
Deep Backtracking Counterfactuals for Causally Compliant Explanations
Klaus-Rudolf Kladny
Julius von Kügelgen
Bernhard Schölkopf
Michael Muehlebach
BDL
547
9
0
11 Oct 2023
GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking
International Conference on Learning Representations (ICLR), 2023
Mert Kosan
S. Verma
Burouj Armgaan
Khushbu Pahwa
Ambuj K. Singh
Sourav Medya
Jignesh M. Patel
465
21
0
03 Oct 2023
ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks
International Conference on Information and Knowledge Management (CIKM), 2023
Yiqiao Li
Jianlong Zhou
Yifei Dong
Niusha Shafiabady
Fang Chen
LLMAG
212
6
0
29 Sep 2023
Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes
Bardh Prenkaj
Mario Villaizán-Vallelado
Tobias Leemann
Gjergji Kasneci
260
2
0
04 Aug 2023
Evaluating Link Prediction Explanations for Graph Neural Networks
Claudio Borile
Alan Perotti
Andre' Panisson
FAtt
244
3
0
03 Aug 2023
Counterfactual Graph Transformer for Traffic Flow Prediction
Yingbin Yang
Kai Du
Xingyuan Dai
Jianwu Fang
AI4TS
360
1
0
01 Aug 2023
1
2
3
Next
Page 1 of 3